Rates of Convergence for Nearest Neighbor Classification
Authors: Kamalika Chaudhuri, Sanjoy Dasgupta
NeurIPS 2014 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | We analyze the behavior of nearest neighbor classification in metric spaces and provide finite-sample, distribution-dependent rates of convergence under minimal assumptions. We illustrate our upper and lower bounds by introducing a new smoothness class customized for nearest neighbor classification. |
| Researcher Affiliation | Academia | Kamalika Chaudhuri Computer Science and Engineering University of California, San Diego kamalika@cs.ucsd.edu Sanjoy Dasgupta Computer Science and Engineering University of California, San Diego dasgupta@cs.ucsd.edu |
| Pseudocode | No | The paper does not contain any structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide any concrete access information (e.g., specific repository link, explicit code release statement) for source code. |
| Open Datasets | No | The paper is theoretical and does not mention using any specific datasets, thus no information on public availability. |
| Dataset Splits | No | The paper is theoretical and does not describe any specific dataset split information (e.g., train/validation/test percentages or counts). |
| Hardware Specification | No | The paper is theoretical and does not provide any specific hardware details used for running experiments. |
| Software Dependencies | No | The paper is theoretical and does not provide any specific ancillary software details with version numbers. |
| Experiment Setup | No | The paper is theoretical and does not describe any specific experimental setup details like hyperparameter values or training configurations. |